Think, explore, & write about what the co-evolutionary interaction between newts & snakes with different genetic architectures (GAs, combination of mutation rate & mutation effect size) can lead to. This markdown is investigation what is up with the different levels of correlation between rectangles and squares (in connection with GA1 tall). After fixing the row vs column error I looked at the correlation data and found that there was less correlation. So I decided to investigate why that might be and run a few more experiments. I am running an experiment to test how changing the square size might impact the calculations. I also plan on changing the interaction rate (but want to look at the math/ feasibility of it). This file contains results discussed in Tall_GA1!
How does cost size impact the spatial correlation of newt and snake phenotypes?
I created a simulation study to observe the co-evolutionary outcome of the newt-snake interaction with different genetic architectures (GAs) in a spatial setting. I hypothesized that we would see an interaction (co-evolutionary arms race) between newt and snake phenotype under some GA combinations when newts and snakes were evolving over geographical space. Each GA is paired with another GA creating 16 combinations.
GA1 experiment values:
Landscape: A tall map!: 35*4 H, 35 W
I tested different cost values:
Each GA combination, trial, and cost has its own msprime simulation.
## All cor, lit, and cost files exist!
## This program will now end!
generations
In order to understand how spatial correlations where changing with time I took 5,000 generation time slices to look at all four trials correlation values. Each color is a different trial per GA combination. The histogram values are stacked. This section only looks at the 5 sections.
Next, we will examine three randomly chosen plots from both the 5 section and 7 section experiment. Time (in generations) in on the x-axis and both mean phenotype and phenotype spatial correlation in on the y-axis. Newt whole population mean phenotype is red, while snake mean phenotype is blue. The pink line is the phenotype spatial correlation.
In the summary section, I try to come up with a way to show how different GA combinations can change the simulations results. In all of these plots snakes GA is represented by color and newt GA is represented by shape. There 16 color-shape combinations (with 4 repeats for trials). There are four sets of plots: 1) newt by snake population size, 2) phenotype difference by snake population size, 3) phenotype difference by snake GA, and 4) phenotype difference by newt GA. There are three figures in each set, taken at the begging, middle, and end time chunks. These are whole population calculations so the 5 section and 7 section data sets are not calculated differently.
##Grid {.tabset}
## [1] -0.4788995
## [1] 0.2678364
## [1] -0.22375
## [1] 0.532094
## [1] 0.4848579